Technological innovations in data storage and processing technologies has led to widespread development and implementation of applications for automatically and rapidly recording transactions and activities of everyday life (e.g., banking, credit card and stock transactions, network performance and usage data, etc.). These application domains typically generate fast, continuous data streams that must be continuously collected and analyzed in real-time (or near-real time) for various purposes (e.g., detecting trends and events of interest, identifying abnormal patterns and anomalies, etc.) depending on the application and nature of the streaming data.
In this regard, there has been extensive research in the data streaming domain to develop data processing techniques for real-time processing (e.g., clustering and classification) of fast and continuous data streams. When developing data stream processing applications, it is important that such applications are designed to extract summary information from the data stream in a manner that allows fast and efficient clustering and classification of the data stream, while minimizing the amount of storage and computation resources needed for processing and storing the summary data.